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Amazon Textract

What it is

Amazon Textract is AWS's managed document text and metadata extraction service — OCR plus form / table / key-value / signature detection over scanned and digital documents. Output is structured JSON with bounding-box positions and per-element confidence scores.

Stub page — expand as additional Textract-internals sources are ingested.

How it appears in architectures

Textract is the canonical OCR + structure-extraction layer in AWS document-intelligence pipelines. It typically sits between a document-upload tier (S3) and a downstream LLM / processing tier (Bedrock + custom logic), invoked by an orchestrator (Lambda):

S3 (upload) → Lambda (orchestrator) → Textract (OCR + metadata)
                                         │
                                         ▼
                              Custom processing / LLM

Capabilities

  • Plain OCR for scanned and digital documents.
  • Forms extraction — key-value pair detection.
  • Tables extraction — row / column / cell structure.
  • Queries — ask natural-language questions directly against the extracted document content.
  • Signature detection, layout analysis, hand-printed text recognition.

Asynchronous-vs-synchronous APIs

Textract supports both synchronous calls (single-page or short documents) and asynchronous job-based APIs (multi-page documents, poll for completion or receive SNS notification).

When to use Textract vs do it yourself

The structural property is managed OCR + structure — Textract handles model maintenance, multi-language support, accuracy improvements over time, and capacity provisioning. The trade-off is pay-per-page pricing versus fixed-cost open-source OCR (e.g. Tesseract) or commercial OCR libraries on self-managed compute.

Composition with downstream LLMs

Textract output is text + structure but is not semantic understanding — converting Textract output into business-meaningful fields typically requires a downstream layer:

  • Rules-based extraction — historical baseline (~55% accuracy on contract-class documents per AArete's pre-2024 system).
  • LLM-based extraction — current state of the art (~99% accuracy on the same contract-class documents per Doczy.ai's AWS-blog-disclosed numbers).
  • Hybrid pipelines with custom preprocessing (smart chunking) and clustering (dual clustering) between Textract and the LLM, which produce "grounded" representations the LLM can reason over more accurately.

Seen in

  • sources/2026-06-02-aws-automating-contract-intelligence-with-doczyai-on-aws — Doczy.ai uses Textract as the OCR + metadata extraction layer before applying smart chunking and dual clustering. "An AWS Lambda function triggers Amazon Textract to extract text and metadata from documents in various formats." Production scale: 50 M pages / 22 months on this pipeline (Textract is invoked once per document; with 2.5 M documents that's ~2.5 M Textract jobs, with per-page billing on the 50 M pages).
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